Motion control of a tensegrity platform

Abstract

Abstract. In this paper, we develop a passive nonlinear constrained particle dynamic model for a class of tensegrity platform structures. Based on the steady state input-output mapping, we then formulate a neural network inversion problem which is later used as the basis for the design of large scale path tracking algorithms.

A Discussion on Control of Tensegrity Systems

“…Kanchanasaratool and Williamson [14], [15], Skelton [16], [17] and de Oliveira [18], [19] have developed different forms of differential-algebraic equations of motion for tensegrity structures. All these representations use nonminimal system coordinates.…”

“…These representations are computationally very efficient, featuring a time-invariant inertia matrix and have no transcendental nonlinearities in coordinate transformations. Kanchanasaratool and Williamson proposed a constrained particle model [14], [15]. Skelton presented a theory for Class 1 structures from momentum and force considerations [16], [17].…”

A novel method of determining the sole configuration of tensegrity structures

“…The tensioned cables of the structure are self-stressed such that the entire system could be provided stable equilibrium before any external loads are added, including gravitational. These smart structures have a large number of potential applications, for the benefit of systems which need, for instance, a small transportation, tunable stiffness properties, active vibration damping and deployment or configuration control [3][4][5][6][7][8][9][10]. Therefore, since tensegrity systems appeared in the early 1950s, the concept of tensegrity has received significant interest among scientists and engineers throughout disciplines such as architecture [11], aerospace [12], civil engineering [13][14][15], robotics [16,17] to biological [18][19][20].…”

Tensegrity frameworks: Dynamic analysis review and open problems

“…Another different approach to the open control of tensegrity structures has been proposed by Kanchanasaratool and Williamson [36]. Since the dynamic model of a tensegrity structure is in general not invertible, and even if the inverse is known to exist, it would be unlikely to find a closed form expression, they proposed to use a neural network to approximate it.…”